{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53079390-42fe-4eb9-8b5a-fce4b1d7a5ad",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib as mpl\n",
    "from scipy.stats import lognorm\n",
    "from scipy.stats import beta \n",
    "import scipy as scp\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rc('font',family = 'Times New Roman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83b022e7-9d15-4150-a49f-581437f174f9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Base_Shear(L):\n",
    "    mm = 1-((L)/(2*R))\n",
    "    thm = np.arctan(mm/(1-mm))\n",
    "    km = (2/(thm)**2)*(1-np.cos(thm))\n",
    "    Wsm = Wtot*(1-mm**2)\n",
    "    Wfm = Wtot-Wsm\n",
    "    Mm=Wsm*km*R+Wfm*R*(1-mm)\n",
    "    Vm = Mm/hi\n",
    "    return Vm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c411d66-f276-46d0-a150-395160ca270d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Rotation_Uplift(theta):\n",
    "    Lm2a=Ly+0.01\n",
    "    Lm2b=0\n",
    "    true = 0\n",
    "    while(true==0):\n",
    "        wm2 = theta/((2/Lm2a)-(1/(2*R)))\n",
    "        N2 = 0.55*(Lm2a-Ly)*p*((kuus/p)/(1+kuus*(Lm2a-Ly)/(A*Es)))**(1/3)\n",
    "        Lm2b = (2*wm2*N2/p)**0.5+Ly\n",
    "        if(abs(Lm2a-Lm2b)<0.001):\n",
    "            true = 1\n",
    "        else:\n",
    "            Lm2a=Lm2b\n",
    "    return wm2,Lm2b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7f78c414-376b-4952-bfa0-ba8633d07147",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "g = 9.805\n",
    "\n",
    "H = 16.5\n",
    "R = 13.9\n",
    "n = 8\n",
    "bw = 2*np.pi*R/n\n",
    "\n",
    "fillr = 0.95\n",
    "\n",
    "ro = 0.998\n",
    "ph = ro*g*H*fillr\n",
    "p = ph*bw\n",
    "\n",
    "tb = 6/1000\n",
    "tw = 17/1000\n",
    "A = bw*tb\n",
    "I = bw*tb**3/12\n",
    "Zp = bw*tb**2/4\n",
    "Es = 210000000\n",
    "v = 0.3 \n",
    "Fys = 235000\n",
    "My = Fys*Zp\n",
    "\n",
    "ros = 7.850 #density of steel\n",
    "Ww = ros*g*H*bw*tw #weight of wall over one side of the strip\n",
    "tr = 31/1000\n",
    "mr = 35\n",
    "Wr = mr*g #weight of roof\n",
    "Wrr = Wr/n\n",
    "mtot = ro*H*fillr*np.pi*R**2\n",
    "Wtot = mtot*g\n",
    "\n",
    "kuu = Es*bw*(tw/R)**1.5/((3*(1-v**2))**0.25)\n",
    "ktt = Es*bw*tw**2*(tw/R)**0.5/(2*(3*(1-v**2))**0.75)\n",
    "ktu = -Es*bw*tw*(tw/R)/(2*(3*(1-v**2))**0.5)\n",
    "\n",
    "kuus = kuu-ktu**2/ktt\n",
    "dtu = -ktu/(ktt*kuu-ktu**2)\n",
    "dtt = kuu/(ktt*kuu-ktu**2)\n",
    "\n",
    "Ly = (6*My/p)**0.5\n",
    "wy = p*Ly**4/(72*Es*I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1c7fac0e-35f6-4700-85cc-74ab1a8c8ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9762961469710678"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = np.linspace(0,145,145, dtype='int')\n",
    "gamma = fillr*H/R\n",
    "vn = np.zeros(len(n))\n",
    "for i in range(len(n)):\n",
    "    vn[i] = np.pi*(2*n[i]+1)/2\n",
    "    \n",
    "I1 = np.zeros(len(n))\n",
    "I1p = np.zeros(len(n))\n",
    "\n",
    "for i in range(len(n)):\n",
    "    I1[i] = scp.special.i1(vn[i]/gamma)\n",
    "    I1p[i] = scp.special.i0(vn[i]/gamma)- scp.special.i1(vn[i]/gamma)/(vn[i]/gamma)\n",
    "\n",
    "mi = np.sum(2*mtot*gamma*(I1/(I1p*vn**3)))\n",
    "hi = H*np.sum((-1)**n*I1*(vn*(-1)**n-1)/(vn**4*I1p))/np.sum((I1/(I1p*vn**3)))\n",
    "Ci = 6.2\n",
    "ro = 998\n",
    "tw = 17.7/1000\n",
    "Es1 = Es*1000\n",
    "Ti = Ci*H*(ro/Es1)**0.5/(tw/R)**0.5\n",
    "ki = 4*np.pi**2*mi/Ti**2\n",
    "10*Ti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eabc0887-5f48-452d-a25f-f18e6df29116",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.583430116180643"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lam1 = 1.8412\n",
    "mc = mtot*2*np.tanh(lam1*gamma)/(lam1*gamma*(lam1**2-1))\n",
    "hc = H*(1+(1-np.cosh(lam1*gamma))/(lam1*gamma*np.sinh(lam1*gamma)))\n",
    "Tc = 2*np.pi*(R/g)**0.5/(lam1*np.tanh(lam1*H/R))**0.5\n",
    "Tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d8fbe7a-4cac-499e-ba96-dd2540fd8728",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def GMSpectra(Tr):\n",
    "    \n",
    "    T = np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_1.txt')[:,0]\n",
    "    Sa = np.zeros((len(np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_1.txt')[:,0]),30))\n",
    "    Sd = np.zeros((len(np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_1.txt')[:,0]),30))\n",
    "\n",
    "    for i in range(len(Sa[0,:])):\n",
    "        Sa[:,i] = np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Acceleration/GMRS_'+str(i+1)+'.txt')[:,1]\n",
    "        Sd[:,i] = np.loadtxt('C:/Users/rober/Documents/ROSE/PostDoc/FRS/NLTH Analyses/Infilled/Ground Motion Scaled/'+Tr+'/ResponseSpectra/Displacement/GMRS_'+str(i+1)+'.txt')[:,1]\n",
    "\n",
    "    Sam = np.zeros(len(Sa[:,0]))\n",
    "    Sdm = np.zeros(len(Sa[:,0]))\n",
    "\n",
    "    for i in range(len(Sam)):\n",
    "        Sam[i] = np.median(Sa[i,:])\n",
    "        Sdm[i] = np.median(Sd[i,:])\n",
    "    return T,Sam,Sdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "4527523a-971c-4543-8c62-0b512ff86cd3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "T,Sam,Sdm = GMSpectra('475')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c3e47889-5563-49d3-9484-3dc6e27a7fbc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absolute Acceleration Spectrum, $S_A$ [g]')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "icon = abs(Tc-T).argmin()\n",
    "plt.plot(T,Sam)\n",
    "plt.xlim(0,6)\n",
    "plt.ylim(0,0.6)\n",
    "plt.plot((Tc,Tc),(0,Sam[icon]),color='k',ls='--')\n",
    "plt.xlabel(r'Period, $T$ [s]',fontsize=12)\n",
    "plt.ylabel(r'Absolute Acceleration Spectrum, $S_A$ [g]',fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "77098b65-d8ea-430b-88e8-6ef2442e29ce",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "586.9973229387336"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vcon = Sam[icon]*g*mc\n",
    "Vcon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6ef707d9-6bc9-46fc-83fc-00d3485eb696",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Absolute Acceleration Spectrum, $S_A$ [g]')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iimp = abs(Ti-T).argmin()\n",
    "plt.plot(T,Sam)\n",
    "plt.xlim(0,2)\n",
    "plt.ylim(0,0.6)\n",
    "plt.plot((Ti,Ti),(0,Sam[iimp]),color='k',ls='--')\n",
    "plt.xlabel(r'Period, $T$ [s]',fontsize=12)\n",
    "plt.ylabel(r'Absolute Acceleration Spectrum, $S_A$ [g]',fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9279588e-e7a3-483e-8b16-0924bcd4eed7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "q = 2\n",
    "Vfd = Sam[iimp]*mi*g/q+Vcon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f697764f-515e-450d-8646-7618b854ec7c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15200.18072925651"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vfd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f8da52b5-bd9b-408c-8e45-79a94e815abf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rober\\AppData\\Local\\Temp\\ipykernel_12920\\2187572073.py:3: RuntimeWarning: divide by zero encountered in divide\n",
      "  thm = np.arctan(mm/(1-mm))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "15126.131358419829"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = np.linspace(0,2,200)\n",
    "iV = abs(Vfd-Base_Shear(L)).argmin()\n",
    "Base_Shear(L[iV])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4634c1c8-ee68-4f0b-a0cf-134e8fc69b3c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8542713567839196"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L[iV]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6f874636-2f64-4ccd-8aad-7bce91889658",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Uplift_SepL(w):\n",
    "    if(w<=wy):\n",
    "        return w*(Ly/wy)\n",
    "    else:\n",
    "        Lm2a=Ly+0.01\n",
    "        Lm2b=0\n",
    "        true = 0\n",
    "        while(true==0):\n",
    "            N2 = 0.55*(Lm2a-Ly)*p*((kuus/p)/(1+kuus*(Lm2a-Ly)/(A*Es)))**(1/3)\n",
    "            Lm2b = (2*w*N2/p)**0.5+Ly\n",
    "            if(abs(Lm2a-Lm2b)<0.0001):\n",
    "                true = 1\n",
    "            else:\n",
    "                Lm2a=Lm2b\n",
    "            #print(Lm2a)\n",
    "        return Lm2b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ae581cde-dbdc-441e-81f9-07b9c1d38a97",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "w = np.linspace(0.1,0.3,500)\n",
    "Lw = np.zeros(len(w))\n",
    "\n",
    "for i in range(len(Lw)):\n",
    "    Lw[i] = Uplift_SepL(w[i])\n",
    "    \n",
    "iw = abs(L[iV]-Lw).argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ab96eb0d-7b90-4f9c-a649-e21b35808f04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2062319900062503"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "th = w[iw]/L[iV]-w[iw]/(2*R)\n",
    "th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "febf6d1b-a139-4083-8233-88657b8a63be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "710df27c-dcb2-4942-ad39-f5faebeed37e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b75dc7d-f557-4241-b611-ecd6b47e1b78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5df1085-a2f0-45dc-ba0c-3a55210a9646",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
